library(png)
format(Sys.time(), format="%d_%b_%Y")
[1] "27_Feb_2017"
## First consolidate the available files into a single table
path <- "~/Box Sync/Four model compare/Module 2 extinct"
setwd(path)
Error in setwd(path) : cannot change working directory
load('~/Box Sync/colliding ranges/Simulations_humans/Results/available daily summaries/Four_model_compare_results_02_Mar_2017_crop_to_3481.Rdata')
extant <- Concatenated_data
extant
setwd("~/Box Sync/colliding ranges/Simulations_humans/Results/available daily summaries")
The working directory was changed to /Users/Ty/Box Sync/colliding ranges/Simulations_humans/Results/available daily summaries inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the the working directory for notebook chunks.
details <- file.info(list.files())
trimmed_details <- details[which(list.files() == list.files(pattern = "Four_model_compare_results_extinct*")),]
ord <- order(trimmed_details$mtime, decreasing = TRUE)
rownames(trimmed_details[ord,])[1]
[1] "Four_model_compare_results_extinct_10_Mar_2017.Rdata"
load(rownames(trimmed_details[ord,])[1])
extinct <- Concatenated_data
trimmed_details <- details[which(list.files() != list.files(pattern = "Four_model_compare_results_extinct*")),]
ord <- order(trimmed_details$mtime, decreasing = TRUE)
rownames(trimmed_details[ord,])[1]
[1] "Four_model_compare_results_10_Mar_2017_crop_to_4897.Rdata"
load(rownames(trimmed_details[ord,])[1])
extant <- Concatenated_data
dim(extinct)
[1] 25492 62
dim(extant)
[1] 14660 62
for(i in c(9,10,11,12,14,15,16,17,19,20,21,22,24,25,26,27,29,30,31,32)){
extinct[which(is.nan(as.numeric(as.character(extinct[, i]))) == TRUE), i] <- NA
}
for(i in c(9,10,11,12,14,15,16,17,19,20,21,22,24,25,26,27,29,30,31,32)){
extant[which(is.nan(as.numeric(as.character(extant[, i]))) == TRUE), i] <- NA
}
i <- 19
for(i in c(20,21,24,25,26,27)){
extinct[which(as.numeric(as.character(extinct[, i])) == 0), i] <- NA
}
for(i in c(20,21,24,25,26,27)){
extant[which(as.numeric(as.character(extant[, i])) == 0), i] <- NA
}
xlimit <- c(0,1)
ylimit <- c(0,600)
maincex <- 0.9
png(file="Global_success_rate_per_parameter.png", width=8.5, height=11, units="in", res=300)
par(mfrow=c(5,4), mar=c(3,3,3,0))
hist(as.numeric(as.character(extinct[,9])), main="speciation of F in F env", col=adjustcolor("firebrick", alpha=0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,9])), main="speciation of F in F env", col=adjustcolor("cornflowerblue", alpha=0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[,10])), main="speciation of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,10])), main="speciation of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[,11])), main="speciation of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,11])), main="speciation of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[,12])), main="speciation of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,12])), main="speciation of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
#######
hist(as.numeric(as.character(extinct[, 14])), main="extinction of F in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 14])), main="extinction of F in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 15])), main="extinction of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 15])), main="extinction of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 16])), main="extinction of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 16])), main="extinction of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 17])), main="extinction of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 17])), main="extinction of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
######
hist(as.numeric(as.character(extinct[, 29])), main="arisal of F in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 29])), main="arisal of F in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 30])), main="arisal of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 30])), main="arisal of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 31])), main="arisal of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 31])), main="arisal of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 32])), main="arisal of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 32])), main="arisal of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
######
hist(as.numeric(as.character(extinct[, 19])), main="NOPE -- Diffusion: source F, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex)
hist(as.numeric(as.character(extant[, 19])), main="NOPE -- Diffusion: source F, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 20])), main="Diffusion: source D, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 20])), main="Diffusion: source D, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 21])), main="Diffusion: source F, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 21])), main="Diffusion: source F, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 22])), main="NOPE -- Diffusion: source D, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex)
hist(as.numeric(as.character(extant[, 22])), main="NOPE -- Diffusion: source D, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex, add=TRUE)
####
hist(as.numeric(as.character(extinct[, 24])), main="Takeover: source F, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 24])), main="Takeover: source F, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 25])), main="Takeover: source D, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 25])), main="Takeover: source D, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 26])), main="Takeover: source F, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 26])), main="Takeover: source F, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
hist(as.numeric(as.character(extinct[, 27])), main="Takeover: source D, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 27])), main="Takeover: source D, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)
dev.off()
null device
1
png(file="extiction minus extant per outcome.png", width=8.5, height=11, units="in", res=300)
par(mfrow=c(3,1))
plot(as.numeric(as.character(extinct[,9])), as.numeric(as.character(extinct[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("firebrick", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))
plot(as.numeric(as.character(extant[,9])), as.numeric(as.character(extant[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("cornflowerblue", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))
plot(as.numeric(as.character(extinct[,9])), as.numeric(as.character(extinct[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("firebrick", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))
points(as.numeric(as.character(extant[,9])), as.numeric(as.character(extant[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("cornflowerblue", alpha=0.2), pch=19, cex=0.6)
dev.off()
null device
1
xfit1 <- seq(xmin, xmax, length= 100)
Error in is.finite(from) :
default method not implemented for type 'closure'
load('~/Box Sync/colliding ranges/Simulations_humans/Available trees/real.analysis.RData')
Concatenated_data <- extant
#Concatenated_data <- Concatenated_data[Concatenated_data[, 2] == "stats.no.bTO", ]
#Concatenated_data <- Concatenated_data[Concatenated_data[, 6] != "05", ]
# Concatenated_data[, 6] <- as.numeric(Concatenated_data[, 6])
# # Concatenated_data[original[, 2] == "background_takeover", 6] <- Concatenated_data[original[, 2] == "background_takeover", 6] + 4
Concatenated_data[, 6] <- factor(Concatenated_data[, 6])
#head(Concatenated_data)
#names(Concatenated_data)
PCAdata <- Concatenated_data[, -(1:35)]
PCAdata <- PCAdata[, -12]
PCAdata <- apply(PCAdata, 2, as.numeric)
remove <- apply(is.na(PCAdata), 1, any)
PCAdata <- PCAdata[!remove, ]
# Predictions
library(randomForest)
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
data.analysis.comp2 <- data.frame("Model" = as.factor(Concatenated_data[!remove, 6]),
PCAdata)
data.analysis.comp2$sprate <- data.analysis.comp2$trait_1_speciation/data.analysis.comp2$trait_2_speciation
data.analysis.comp2$extrate <- data.analysis.comp2$trait_1_extinction/data.analysis.comp2$trait_2_extinction
#load("Real_phy/real.analysis.RData")
a <- as.data.frame(real.analysis$results_summary_of_single_value_outputs)
a$sprate <- a$trait_1_speciation / a$trait_2_speciation
a$extrate <- a$trait_1_extinction / a$trait_2_extinction
data.analysis.comp3 <- data.analysis.comp2[, -c(2, 13:14, 16:20)]
#data.analysis.comp3 <- data.analysis.comp3[data.analysis.comp3$Model %in% 1:4, ]
#data.analysis.comp3$Model <- factor(data.analysis.comp3$Model)
#sub <- unlist(lapply(as.list(c(1:4)), function(x, y) {
# sample(which(y$Model == x), min(table(data.analysis.comp3$Model)))},
# y = data.analysis.comp3))
# data.analysis.comp3 <- data.analysis.comp3[sub, ]
fun <- function(x, y, per = .33) {sample(which(y$Model == x), round(table(y$Model)[1]*per))}
sub.test <- unlist(lapply(as.list(paste0(0, c(1:4))), fun,
y = data.analysis.comp3))
test2 <- data.analysis.comp3[sub.test, 2:ncol(data.analysis.comp3)]
test1 <- data.analysis.comp3[sub.test, 1]
train <- data.analysis.comp3[-sub.test, ]
for(i in 1:100){
(fit <- randomForest(Model ~ ., data=train, xtest = test2, ytest = test1,
importance=TRUE, ntree=1000, keep.forest = TRUE, replace=FALSE))
predictions <- predict(fit,
a,
type="prob")
predictions
save(fit, file=paste0("~/Box Sync/colliding ranges/Simulations_humans/Results/RF_daily_output/RF_daily_output_", format(Sys.time(), format="%d_%b_%Y"), "_",i, "_NoREPLACEMENT_.Rdata"))
}
save(fit, file=paste0("~/Box Sync/colliding ranges/Simulations_humans/Results/RF_daily_output/RF_daily_output_", format(Sys.time(), format="%d_%b_%Y"),".Rdata"))
plot(fit, ylim=c(0,1))

labs <- c("Basic", "+Diffusion", "+Takeover", "+Diffusion +Takeover")
# bar plot
png("Prob_aus.png", width = 25, height = 25, res = 300, units = "in")
par(mar = c(8, 8, 1, 1))
pred <- setNames(as.numeric(predictions), labs)
cols <- rev(c("darkgreen", "red", "blue", "darkorange1"))
barplot(pred, col = cols, ylab = "Proability", cex.lab = 3, cex.names = 2)
dev.off()
null device
1
# Plot confusion matrix
png("Conffusion_matrix_all.png", width = 25, height = 25, res=300, units="in")
par(mar = c(10, 11, 1, 1))
colors1 <- colorRampPalette(colors = c("#f0f0f0", "#bdbdbd","#636363"))
prop <- apply(fit$confusion[, -5], 2, function(x){x / sum(x)}) * 100
image(prop, col = colors1(20), axes=FALSE)
axis(1, at=c(0, .33, .66, 1), labels=labs, tick = FALSE, line = FALSE, cex.axis = 3.5, pos = -.19)
axis(2, at=c(0, .33, .66, 1), labels=labs, tick = FALSE, line = FALSE, cex.axis = 3.5)
mtext("ACTUAL", side = 1, padj = 3, cex = 4)
mtext("PREDICTED", side = 2, padj = -3, cex = 4)
for(i in 1:4) {
for(j in 1:4) {
text(x = c(0, .33, .66, 1)[i], y = c(0, .33, .66, 1)[j], paste0(round(prop[i, j], 2), "%"),
cex = 5)
}
}
dev.off()
null device
1
importance(fit)
01 02 03
Pylo_diversity_is_sum_of_BL 36.68603 25.692246 44.88765
average_phylogenetic_diversity_is_mean_of_BL 43.12789 34.908001 47.98480
variance_Pylo_diversity_is_variance_of_BL 38.00313 20.262707 19.18043
F_quadratic_entropy_is_sum_of_PD 28.49584 23.877597 40.27946
Mean_pairwise_distance 35.73981 27.249223 32.09020
variance_pairwise_distance 39.27909 38.069218 40.92584
Evolutionary_distinctiveness_sum 37.35990 28.081845 45.31515
mean_Phylogenetic_isolation 41.20750 35.209163 48.94046
variance_Phylogenetic_isolation 35.88075 6.978819 28.67514
gamma 27.59011 28.762859 76.57843
extinction_per_speciation 11.04195 51.084694 65.78277
transition_from_trait_1_to_2 28.53323 27.846818 47.04102
transition_from_trait_2_to_1 36.78696 30.942883 50.69606
transition_rate_ratio_1to2_over_2to1 54.87487 38.192677 38.58033
Phylogenetic_signal 66.29483 45.359260 70.57335
spatial.tests.fora 53.03264 95.790134 98.20864
spatial.tests.dom 86.83866 40.905940 53.51233
prevalence 55.09973 15.677005 39.96226
sprate 57.87711 32.873304 105.88921
extrate 29.72153 29.191046 40.11454
04 MeanDecreaseAccuracy
Pylo_diversity_is_sum_of_BL 45.89839 67.10212
average_phylogenetic_diversity_is_mean_of_BL 35.72290 71.53199
variance_Pylo_diversity_is_variance_of_BL 31.31532 59.79845
F_quadratic_entropy_is_sum_of_PD 42.73530 76.03108
Mean_pairwise_distance 42.95949 69.53536
variance_pairwise_distance 37.44388 76.20714
Evolutionary_distinctiveness_sum 46.70827 67.45126
mean_Phylogenetic_isolation 36.35073 72.84609
variance_Phylogenetic_isolation 23.59933 50.96412
gamma 51.86023 104.53469
extinction_per_speciation 53.99058 87.53765
transition_from_trait_1_to_2 36.98221 64.93037
transition_from_trait_2_to_1 42.85645 68.22442
transition_rate_ratio_1to2_over_2to1 51.09836 93.31104
Phylogenetic_signal 46.37239 91.08957
spatial.tests.fora 76.18005 138.82996
spatial.tests.dom 64.49868 119.35092
prevalence 64.89918 86.99762
sprate 41.98590 109.24396
extrate 37.94293 69.14048
MeanDecreaseGini
Pylo_diversity_is_sum_of_BL 271.5521
average_phylogenetic_diversity_is_mean_of_BL 241.4081
variance_Pylo_diversity_is_variance_of_BL 222.2757
F_quadratic_entropy_is_sum_of_PD 247.9364
Mean_pairwise_distance 272.9117
variance_pairwise_distance 259.1696
Evolutionary_distinctiveness_sum 277.8165
mean_Phylogenetic_isolation 241.4813
variance_Phylogenetic_isolation 220.4116
gamma 283.5771
extinction_per_speciation 340.7512
transition_from_trait_1_to_2 335.8418
transition_from_trait_2_to_1 356.5606
transition_rate_ratio_1to2_over_2to1 288.0278
Phylogenetic_signal 418.6853
spatial.tests.fora 536.9193
spatial.tests.dom 344.7908
prevalence 303.3840
sprate 342.5898
extrate 260.9412
# Variables importance
imp <- importance(fit)
imp <- apply(imp, 2, function(x) (x - min(x))/(max(x) - min(x)))
imp <- imp[sort(imp[, 5], index.return = TRUE, decreasing = TRUE)$ix, ]
names <- rownames(imp)
names[names == "spatial.tests.fora"] <- "Space F"
names[names == "spatial.tests.dom"] <- "Space D"
names[names == "sprate"] <- "Sp(ratio)"
names[names == "transition_from_trait_1_to_2"] <- "TR(1-2)"
names[names == "transition_from_trait_2_to_1"] <- "TR(2-1)"
names[names == "Phylogenetic_signal"] <- "PhySig(D)"
names[names == "Evolutionary_distinctiveness_sum"] <- "EDsum"
names[names == "Pylo_diversity_is_sum_of_BL"] <- "PDsum"
names[names == "transition_rate_ratio_1to2_over_2to1"] <- "TR(ratio)"
names[names == "gamma"] <- "Gamma"
names[names == "mean_Phylogenetic_isolation"] <- "MPI"
names[names == "extrate"] <- "Ext(ratio)"
names[names == "average_phylogenetic_diversity_is_mean_of_BL"] <- "PDmean"
names[names == "extinction_per_speciation"] <- "DR"
names[names == "variance_Phylogenetic_isolation"] <- "VPI"
names[names == "F_quadratic_entropy_is_sum_of_PD"] <- "F"
names[names == "Mean_pairwise_distance"] <- "MPD"
names[names == "variance_Pylo_diversity_is_variance_of_BL"] <- "PDvar"
names[names == "variance_pairwise_distance"] <- "VPD"
png("var_import_all.png", width = 25, height = 25, unit="in", res=300)
par(mar = c(10, 18, 1, 1))
plot(x = rev(imp[, 5]), y = 1:nrow(imp), type = "l", yaxt = "n",
ylab = "", xlab = "Variable Importance",
xlim = c(0, 1), lwd = 2, cex.lab = 4)
for (i in 1:nrow(imp)) {
abline(h = i, lty = 3, col = "gray80")
}
abline(v = seq(0, 1, 1/19), lty = 3, col = "gray80")
lines(x = rev(imp[, 4]), y = 1:nrow(imp), col = "darkgreen", lwd = 2)
lines(x = rev(imp[, 3]), y = 1:nrow(imp), col = "red", lwd = 2)
lines(x = rev(imp[, 2]), y = 1:nrow(imp), col = "blue", lwd = 2)
lines(x = rev(imp[, 1]), y = 1:nrow(imp), col = "darkorange1", lwd = 2)
lines(x = rev(imp[, 5]), y = 1:nrow(imp), lwd = 3)
points(x = rev(imp[, 4]), y = 1:nrow(imp), col = "darkgreen", cex = 2)
points(x = rev(imp[, 3]), y = 1:nrow(imp), col = "red", cex = 2)
points(x = rev(imp[, 2]), y = 1:nrow(imp), col = "blue", cex = 2)
points(x = rev(imp[, 1]), y = 1:nrow(imp), col = "darkorange1", cex = 2)
points(x = rev(imp[, 5]), y = 1:nrow(imp), pch = 20, cex = 3)
text(y = 1:nrow(imp), x = par("usr")[1] - .17, labels = rev(names),
srt = 0, pos = 4, xpd = T, cex = 4)
dev.off()
null device
1
par(mfrow=c(2,3))
# Box plots
boxplot(spatial.tests.fora ~ Model, data = data.analysis.comp3)
abline(h = a$spatial.tests.fora, col = "red", lty = 2)
boxplot(spatial.tests.dom ~ Model, data = data.analysis.comp3)
abline(h = a$spatial.tests.fora, col = "red", lty = 2)
boxplot(log(sprate) ~ Model, data = data.analysis.comp3, ylim = c(-10, 10))
abline(h = log(a$sprate), col = "red", lty = 2)
boxplot(log(extrate) ~ Model, data = data.analysis.comp3, ylim = c(-10, 10))
abline(h = log(a$extrate), col = "red", lty = 2)
boxplot(log(transition_rate_ratio_1to2_over_2to1) ~ Model, data = data.analysis.comp3)
abline(h = log(a$sprate), col = "red", lty = 2)
boxplot(Phylogenetic_signal ~ Model, data = data.analysis.comp3, ylim = c(0, 1))
abline(h = a$Phylogenetic_signal, col = "red", lty = 2)

str(fit)
List of 19
$ call : language randomForest(formula = Model ~ ., data = train, xtest = test2, ytest = test1, importance = TRUE, ntree = 2400, keep.forest = TRUE)
$ type : chr "classification"
$ predicted : Factor w/ 4 levels "01","02","03",..: 1 3 1 1 3 3 3 1 3 1 ...
..- attr(*, "names")= chr [1:8885] "1" "2" "3" "4" ...
$ err.rate : num [1:2400, 1:5] 0.547 0.547 0.549 0.535 0.533 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : NULL
.. ..$ : chr [1:5] "OOB" "01" "02" "03" ...
$ confusion : num [1:4, 1:5] 1392 298 434 90 230 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:4] "01" "02" "03" "04"
.. ..$ : chr [1:5] "01" "02" "03" "04" ...
$ votes : matrix [1:8885, 1:4] 0.549 0.346 0.906 0.902 0.22 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:8885] "1" "2" "3" "4" ...
.. ..$ : chr [1:4] "01" "02" "03" "04"
..- attr(*, "class")= chr [1:2] "matrix" "votes"
$ oob.times : num [1:8885] 907 876 855 911 850 863 879 836 883 902 ...
$ classes : chr [1:4] "01" "02" "03" "04"
$ importance : num [1:20, 1:6] 0.0248 0.0262 0.0149 0.012 0.0186 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:20] "Pylo_diversity_is_sum_of_BL" "average_phylogenetic_diversity_is_mean_of_BL" "variance_Pylo_diversity_is_variance_of_BL" "F_quadratic_entropy_is_sum_of_PD" ...
.. ..$ : chr [1:6] "01" "02" "03" "04" ...
$ importanceSD : num [1:20, 1:5] 0.000668 0.000584 0.00033 0.000355 0.000494 ...
..- attr(*, "dimnames")=List of 2
.. ..$ : chr [1:20] "Pylo_diversity_is_sum_of_BL" "average_phylogenetic_diversity_is_mean_of_BL" "variance_Pylo_diversity_is_variance_of_BL" "F_quadratic_entropy_is_sum_of_PD" ...
.. ..$ : chr [1:5] "01" "02" "03" "04" ...
$ localImportance: NULL
$ proximity : NULL
$ ntree : num 2400
$ mtry : num 4
$ forest :List of 14
..$ ndbigtree : int [1:2400] 3853 3761 3829 3805 3823 3977 3887 3917 3887 3907 ...
..$ nodestatus: int [1:4035, 1:2400] 1 1 1 1 1 1 1 1 1 1 ...
..$ bestvar : int [1:4035, 1:2400] 13 8 11 16 7 17 10 7 18 11 ...
..$ treemap : int [1:4035, 1:2, 1:2400] 2 4 6 8 10 12 14 16 18 20 ...
..$ nodepred : int [1:4035, 1:2400] 0 0 0 0 0 0 0 0 0 0 ...
..$ xbestsplit: num [1:4035, 1:2400] 193.4137 0.0287 0.9995 25.8679 33.4238 ...
..$ pid : num [1:4] 1 1 1 1
..$ cutoff : num [1:4] 0.25 0.25 0.25 0.25
..$ ncat : Named int [1:20] 1 1 1 1 1 1 1 1 1 1 ...
.. ..- attr(*, "names")= chr [1:20] "Pylo_diversity_is_sum_of_BL" "average_phylogenetic_diversity_is_mean_of_BL" "variance_Pylo_diversity_is_variance_of_BL" "F_quadratic_entropy_is_sum_of_PD" ...
..$ maxcat : int 1
..$ nrnodes : int 4035
..$ ntree : num 2400
..$ nclass : int 4
..$ xlevels :List of 20
.. ..$ Pylo_diversity_is_sum_of_BL : num 0
.. ..$ average_phylogenetic_diversity_is_mean_of_BL: num 0
.. ..$ variance_Pylo_diversity_is_variance_of_BL : num 0
.. ..$ F_quadratic_entropy_is_sum_of_PD : num 0
.. ..$ Mean_pairwise_distance : num 0
.. ..$ variance_pairwise_distance : num 0
.. ..$ Evolutionary_distinctiveness_sum : num 0
.. ..$ mean_Phylogenetic_isolation : num 0
.. ..$ variance_Phylogenetic_isolation : num 0
.. ..$ gamma : num 0
.. ..$ extinction_per_speciation : num 0
.. ..$ transition_from_trait_1_to_2 : num 0
.. ..$ transition_from_trait_2_to_1 : num 0
.. ..$ transition_rate_ratio_1to2_over_2to1 : num 0
.. ..$ Phylogenetic_signal : num 0
.. ..$ spatial.tests.fora : num 0
.. ..$ spatial.tests.dom : num 0
.. ..$ prevalence : num 0
.. ..$ sprate : num 0
.. ..$ extrate : num 0
$ y : Factor w/ 4 levels "01","02","03",..: 1 1 1 1 1 1 1 1 1 1 ...
..- attr(*, "names")= chr [1:8885] "1" "2" "3" "4" ...
$ test :List of 5
..$ predicted: Factor w/ 4 levels "01","02","03",..: 1 3 1 1 1 3 1 3 2 1 ...
.. ..- attr(*, "names")= chr [1:188] "1410" "86" "2307" "1834" ...
..$ err.rate : num [1:2400, 1:5] 0.559 0.537 0.473 0.479 0.473 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : NULL
.. .. ..$ : chr [1:5] "Test" "01" "02" "03" ...
..$ confusion: num [1:4, 1:5] 27 6 10 4 6 24 1 8 11 2 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:4] "01" "02" "03" "04"
.. .. ..$ : chr [1:5] "01" "02" "03" "04" ...
..$ votes : matrix [1:188, 1:4] 0.94 0.354 0.532 0.543 0.525 ...
.. ..- attr(*, "dimnames")=List of 2
.. .. ..$ : chr [1:188] "1410" "86" "2307" "1834" ...
.. .. ..$ : chr [1:4] "01" "02" "03" "04"
.. ..- attr(*, "class")= chr [1:2] "matrix" "votes"
..$ proximity: NULL
$ inbag : NULL
$ terms :Classes 'terms', 'formula' language Model ~ Pylo_diversity_is_sum_of_BL + average_phylogenetic_diversity_is_mean_of_BL + variance_Pylo_diversity_is_variance_of_BL + F_quadratic_entropy_is_sum_of_PD + ...
.. ..- attr(*, "variables")= language list(Model, Pylo_diversity_is_sum_of_BL, average_phylogenetic_diversity_is_mean_of_BL, variance_Pylo_diversity_is_variance_of_BL, F_quadratic_entropy_is_sum_of_PD, ...
.. ..- attr(*, "factors")= int [1:21, 1:20] 0 1 0 0 0 0 0 0 0 0 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:21] "Model" "Pylo_diversity_is_sum_of_BL" "average_phylogenetic_diversity_is_mean_of_BL" "variance_Pylo_diversity_is_variance_of_BL" ...
.. .. .. ..$ : chr [1:20] "Pylo_diversity_is_sum_of_BL" "average_phylogenetic_diversity_is_mean_of_BL" "variance_Pylo_diversity_is_variance_of_BL" "F_quadratic_entropy_is_sum_of_PD" ...
.. ..- attr(*, "term.labels")= chr [1:20] "Pylo_diversity_is_sum_of_BL" "average_phylogenetic_diversity_is_mean_of_BL" "variance_Pylo_diversity_is_variance_of_BL" "F_quadratic_entropy_is_sum_of_PD" ...
.. ..- attr(*, "order")= int [1:20] 1 1 1 1 1 1 1 1 1 1 ...
.. ..- attr(*, "intercept")= num 0
.. ..- attr(*, "response")= int 1
.. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. ..- attr(*, "predvars")= language list(Model, Pylo_diversity_is_sum_of_BL, average_phylogenetic_diversity_is_mean_of_BL, variance_Pylo_diversity_is_variance_of_BL, F_quadratic_entropy_is_sum_of_PD, ...
.. ..- attr(*, "dataClasses")= Named chr [1:21] "factor" "numeric" "numeric" "numeric" ...
.. .. ..- attr(*, "names")= chr [1:21] "Model" "Pylo_diversity_is_sum_of_BL" "average_phylogenetic_diversity_is_mean_of_BL" "variance_Pylo_diversity_is_variance_of_BL" ...
- attr(*, "class")= chr [1:2] "randomForest.formula" "randomForest"
---
title: "D-place FARM documentation: Module 3"
author: "Ty Tuff, Bruno Vilela, and Carlos Botero"
date: 'project began: 15 May 2016, document updated: `r strftime(Sys.time(), format
  = "%d %B %Y")`'
output:
  html_notebook: default
  html_document: default
  pdf_document: default
  word_document: default
bibliography: FARM package.bib
---
```{r}
library(png)
```



```{r}
## First consolidate the available files into a single table
    
      path <- "~/Box Sync/Four model compare/Module 2"
           
     
           setwd(path)
    myfiles_full <- list.dirs()
    analyze_this_many <- length(myfiles_full)
    
    available_files <- matrix(NA, 1, 1)
    
        
    for(i in 1: analyze_this_many){
    available_files <- rbind(available_files , as.matrix(list.files(myfiles_full[i], full.names = TRUE)))
    }
    dim(available_files)
    
    split.file.name <- strsplit(available_files[10], split = "_") 
    
    
    
 
available <- list.files()
files <- matrix(rep(NA, 62), length(available), 62)
dim(files)
i <- 10


for(i in 1:length(available)){
load(available[i])
name <- unlist(strsplit(available[i], split="_"))
files[i,] <- c(as.vector(matrix(name, 1,35)),matrix(Sim_statistics[[1]], 1, 27))

}


colnames(files) <-  c(

	NA,
	"background_takeover_type" ,
	NA,
	"replicate",
	NA,
	"Model_type",
	rep(NA,2),
	"speciation_of_Env_NonD",
	"speciation_of_Env_D",
	"speciation_of_For",
	"speciation_of_Dom",
	NA,
	"extinction_of_Env_NonD",
	"extinction_of_Env_D",
	"extinction_of_For",
	"extinction_of_Dom",
	NA,
	"P.diffusion_Target_forager",
	"P.diffusion_Target_domesticator",
	"P.diffusion_Source_forager",
	"P.diffusion_Source_domesticator",
	NA,
	"P.takeover_Target_forager",
	"P.takeover_Target_domesticator",
	"P.takeover_Source_forager",
	"P.takeover_Source_domesticator",
	NA,
	"arisal_of_Env_NonD",
	"arisal_of_Env_D",
	"arisal_of_For",
	"arisal_of_Dom",
	
	NA, 
	"timesteps", 
	NA,
        
    "number_of_branches",
	"Pylo_diversity_is_sum_of_BL",
	"average_phylogenetic_diversity_is_mean_of_BL",
	"variance_Pylo_diversity_is_variance_of_BL",

	"F_quadratic_entropy_is_sum_of_PD",
	"Mean_pairwise_distance",
	"variance_pairwise_distance",

	"Evolutionary_distinctiveness_sum",
	"mean_Phylogenetic_isolation",
	"variance_Phylogenetic_isolation",

	"gamma",
	"gamma_p_value",
	"speciation_rate",
	"extinction_rate",
	"extinction_per_speciation",
	"speciation_minus_extinction",
	"trait_1_speciation",
  	"trait_2_speciation" ,
  	"trait_1_extinction" ,
  	"trait_2_extinction" ,
  	"transition_from_trait_1_to_2" ,
  	"transition_from_trait_2_to_1" ,
  	"transition_rate_ratio_1to2_over_2to1" ,
  	"Phylogenetic_signal",
  	"spatial.tests.fora",
  	"spatial.tests.dom",
  	"prevalence"
  	
    
  )

results_table <- as.data.frame(files)
head(results_table)
dim(results_table)
Concatenated_data <- results_table
save(Concatenated_data, file="~/Desktop/Four_model_compare_results.Rdata")

one <- subset(results_table, Model_type=="01" )
two <- subset(results_table, Model_type=="02" )
three <- subset(results_table, Model_type=="03" )
four <- subset(results_table, Model_type=="04" )
crop <- min(length(one[,1]),
length(two[,1]),
length(three[,1]),
length(four[,1]))
one <- one[1:crop,]
two <- two[1:crop,]
three <- three[1:crop,]
four <- four[1:crop,]

Concatenated_data <- rbind(one, two, three, four)
dim(Concatenated_data)





save(Concatenated_data, file=paste0("~/Box Sync/colliding ranges/Simulations_humans/Results/available daily summaries/Four_model_compare_results", format(Sys.time(), format="%d_%b_%Y"),"_crop_to_", crop,".Rdata"))
crop

```




```{r}
## First consolidate the available files into a single table
    
      path <- "~/Box Sync/Four model compare/Module 2 extinct"
           
     
           setwd(path)
    myfiles_full <- list.dirs()
    analyze_this_many <- length(myfiles_full)
    
    available_files <- matrix(NA, 1, 1)
    
        
    for(i in 1: analyze_this_many){
    available_files <- rbind(available_files , as.matrix(list.files(myfiles_full[i], full.names = TRUE)))
    }
    dim(available_files)
    
    split.file.name <- strsplit(available_files[10], split = "_") 
    
    
    
 
available <- list.files()
files <- matrix(rep(NA, 62), length(available), 62)
dim(files)
i <- 10


for(i in 1:length(available)){
load(available[i])
name <- unlist(strsplit(available[i], split="_"))
files[i,] <- c(as.vector(matrix(name, 1,35)),matrix(Sim_statistics[[1]], 1, 27))

}


colnames(files) <-  c(

	NA,
	"background_takeover_type" ,
	NA,
	"replicate",
	NA,
	"Model_type",
	rep(NA,2),
	"speciation_of_Env_NonD",
	"speciation_of_Env_D",
	"speciation_of_For",
	"speciation_of_Dom",
	NA,
	"extinction_of_Env_NonD",
	"extinction_of_Env_D",
	"extinction_of_For",
	"extinction_of_Dom",
	NA,
	"P.diffusion_Target_forager",
	"P.diffusion_Target_domesticator",
	"P.diffusion_Source_forager",
	"P.diffusion_Source_domesticator",
	NA,
	"P.takeover_Target_forager",
	"P.takeover_Target_domesticator",
	"P.takeover_Source_forager",
	"P.takeover_Source_domesticator",
	NA,
	"arisal_of_Env_NonD",
	"arisal_of_Env_D",
	"arisal_of_For",
	"arisal_of_Dom",
	
	NA, 
	"timesteps", 
	NA,
        
    "number_of_branches",
	"Pylo_diversity_is_sum_of_BL",
	"average_phylogenetic_diversity_is_mean_of_BL",
	"variance_Pylo_diversity_is_variance_of_BL",

	"F_quadratic_entropy_is_sum_of_PD",
	"Mean_pairwise_distance",
	"variance_pairwise_distance",

	"Evolutionary_distinctiveness_sum",
	"mean_Phylogenetic_isolation",
	"variance_Phylogenetic_isolation",

	"gamma",
	"gamma_p_value",
	"speciation_rate",
	"extinction_rate",
	"extinction_per_speciation",
	"speciation_minus_extinction",
	"trait_1_speciation",
  	"trait_2_speciation" ,
  	"trait_1_extinction" ,
  	"trait_2_extinction" ,
  	"transition_from_trait_1_to_2" ,
  	"transition_from_trait_2_to_1" ,
  	"transition_rate_ratio_1to2_over_2to1" ,
  	"Phylogenetic_signal",
  	"spatial.tests.fora",
  	"spatial.tests.dom",
  	"prevalence"
  	
    
  )

Concatenated_data <- as.data.frame(files)
head(Concatenated_data)
dim(Concatenated_data)

save(Concatenated_data, file=paste0("~/Box Sync/colliding ranges/Simulations_humans/Results/available daily summaries/Four_model_compare_results_extinct_", format(Sys.time(), format="%d_%b_%Y"),"_crop_to_", crop,".Rdata"))

```


```{r}
load('~/Box Sync/colliding ranges/Simulations_humans/Results/available daily summaries/Four_model_compare_results_02_Mar_2017_crop_to_3481.Rdata')
extant <- Concatenated_data
extant
```




```{r}
setwd("~/Box Sync/colliding ranges/Simulations_humans/Results/available daily summaries")
details <- file.info(list.files())

trimmed_details <- details[which(list.files() == list.files(pattern = "Four_model_compare_results_extinct*")),]
ord <- order(trimmed_details$mtime, decreasing = TRUE)
rownames(trimmed_details[ord,])[1]
load(rownames(trimmed_details[ord,])[1])
extinct <- Concatenated_data

trimmed_details <- details[which(list.files() != list.files(pattern = "Four_model_compare_results_extinct*")),]
ord <- order(trimmed_details$mtime, decreasing = TRUE)
rownames(trimmed_details[ord,])[1]
load(rownames(trimmed_details[ord,])[1])
extant <- Concatenated_data





```

```{r}
dim(extinct)
dim(extant)
```




```{r}

for(i in c(9,10,11,12,14,15,16,17,19,20,21,22,24,25,26,27,29,30,31,32)){
	extinct[which(is.nan(as.numeric(as.character(extinct[, i]))) == TRUE), i] <- NA
}

for(i in c(9,10,11,12,14,15,16,17,19,20,21,22,24,25,26,27,29,30,31,32)){
	extant[which(is.nan(as.numeric(as.character(extant[, i]))) == TRUE), i] <- NA
}

i <- 19
for(i in c(20,21,24,25,26,27)){
	extinct[which(as.numeric(as.character(extinct[, i])) == 0), i] <- NA
}

for(i in c(20,21,24,25,26,27)){
	extant[which(as.numeric(as.character(extant[, i])) == 0), i] <- NA
}


xlimit <- c(0,1)
ylimit <- c(0,600)
maincex <- 0.9

png(file="Global_success_rate_per_parameter.png", width=8.5, height=11, units="in", res=300)

par(mfrow=c(5,4), mar=c(3,3,3,0))


hist(as.numeric(as.character(extinct[,9])), main="speciation of F in F env", col=adjustcolor("firebrick", alpha=0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,9])), main="speciation of F in F env", col=adjustcolor("cornflowerblue", alpha=0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)


hist(as.numeric(as.character(extinct[,10])), main="speciation of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,10])), main="speciation of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)


hist(as.numeric(as.character(extinct[,11])), main="speciation of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,11])), main="speciation of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[,12])), main="speciation of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[,12])), main="speciation of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

#######

hist(as.numeric(as.character(extinct[, 14])), main="extinction of F in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 14])), main="extinction of F in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)



hist(as.numeric(as.character(extinct[, 15])), main="extinction of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 15])), main="extinction of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 16])), main="extinction of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 16])), main="extinction of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 17])), main="extinction of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 17])), main="extinction of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

######

hist(as.numeric(as.character(extinct[, 29])), main="arisal of F in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 29])), main="arisal of F in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)



hist(as.numeric(as.character(extinct[, 30])), main="arisal of D in F env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 30])), main="arisal of D in F env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 31])), main="arisal of F in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 31])), main="arisal of F in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 32])), main="arisal of D in D env", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 32])), main="arisal of D in D env", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

######

hist(as.numeric(as.character(extinct[, 19])), main="NOPE -- Diffusion: source F, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex)
hist(as.numeric(as.character(extant[, 19])), main="NOPE -- Diffusion: source F, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex, add=TRUE)



hist(as.numeric(as.character(extinct[, 20])), main="Diffusion: source D, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 20])), main="Diffusion: source D, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 21])), main="Diffusion: source F, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 21])), main="Diffusion: source F, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 22])), main="NOPE -- Diffusion: source D, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex)
hist(as.numeric(as.character(extant[, 22])), main="NOPE -- Diffusion: source D, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= c(0,18000), cex.main= maincex, add=TRUE)

####

hist(as.numeric(as.character(extinct[, 24])), main="Takeover: source F, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 24])), main="Takeover: source F, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)



hist(as.numeric(as.character(extinct[, 25])), main="Takeover: source D, target F", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 25])), main="Takeover: source D, target F", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 26])), main="Takeover: source F, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 26])), main="Takeover: source F, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)

hist(as.numeric(as.character(extinct[, 27])), main="Takeover: source D, target D", col=adjustcolor("firebrick", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex)
hist(as.numeric(as.character(extant[, 27])), main="Takeover: source D, target D", col=adjustcolor("cornflowerblue", alpha= 0.7), breaks=100, border=NA, xlim= xlimit, ylim= ylimit, cex.main= maincex, add=TRUE)


dev.off()






```


![](Global_success_rate_per_parameter.png)


```{r}



png(file="extiction minus extant per outcome.png", width=8.5, height=11, units="in", res=300)
par(mfrow=c(3,1))

plot(as.numeric(as.character(extinct[,9])), as.numeric(as.character(extinct[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("firebrick", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))
plot(as.numeric(as.character(extant[,9])), as.numeric(as.character(extant[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("cornflowerblue", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))

plot(as.numeric(as.character(extinct[,9])), as.numeric(as.character(extinct[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("firebrick", alpha=0.2), pch=19, cex=0.6, ylim=c(0,1))
points(as.numeric(as.character(extant[,9])), as.numeric(as.character(extant[,14])), xlab="speciation", ylab="extinction", col= adjustcolor("cornflowerblue", alpha=0.2), pch=19, cex=0.6)


dev.off()


```

![](extiction minus extant per outcome.png)



```{r}

params <- extant[,-1:-35]
names(params)
break_number <- 10
xmin <-
xmax <- 

x1 <- as.numeric(params[,1])
h1 <- hist(x1,  plot=FALSE, breaks= break_number)
xfit1 <- seq(xmin, xmax, length= 100)
yfit1 <- dnorm(xfit1, mean=mean(x1), sd=sd(x1))
yfit1 <- yfit1*diff(h1$mids[1:2])*length(x1)+101.5
polygon(xfit1, yfit1, col=adjustcolor("limegreen", alpha=0.5), lwd=2, border=adjustcolor("limegreen", alpha=0.6))

```





```{r}


load('~/Box Sync/colliding ranges/Simulations_humans/Available trees/real.analysis.RData')

Concatenated_data <- extant

#Concatenated_data <- Concatenated_data[Concatenated_data[, 2] == "stats.no.bTO", ]
#Concatenated_data <- Concatenated_data[Concatenated_data[, 6] != "05", ]
# Concatenated_data[, 6] <- as.numeric(Concatenated_data[, 6])
# # Concatenated_data[original[, 2] == "background_takeover", 6] <-  Concatenated_data[original[, 2] == "background_takeover", 6] + 4
Concatenated_data[, 6] <- factor(Concatenated_data[, 6])
#head(Concatenated_data)
#names(Concatenated_data)

PCAdata <- Concatenated_data[, -(1:35)]
PCAdata <- PCAdata[, -12]
PCAdata <- apply(PCAdata, 2, as.numeric)
remove <- apply(is.na(PCAdata), 1, any)
PCAdata <- PCAdata[!remove, ]

# Predictions
library(randomForest)

data.analysis.comp2 <- data.frame("Model" = as.factor(Concatenated_data[!remove, 6]),
                                  PCAdata)
data.analysis.comp2$sprate <- data.analysis.comp2$trait_1_speciation/data.analysis.comp2$trait_2_speciation
data.analysis.comp2$extrate <- data.analysis.comp2$trait_1_extinction/data.analysis.comp2$trait_2_extinction


#load("Real_phy/real.analysis.RData")
a <- as.data.frame(real.analysis$results_summary_of_single_value_outputs)
a$sprate <- a$trait_1_speciation / a$trait_2_speciation
a$extrate <- a$trait_1_extinction / a$trait_2_extinction

data.analysis.comp3 <- data.analysis.comp2[, -c(2, 13:14, 16:20)]
#data.analysis.comp3 <- data.analysis.comp3[data.analysis.comp3$Model %in% 1:4, ]
#data.analysis.comp3$Model <- factor(data.analysis.comp3$Model)
#sub <- unlist(lapply(as.list(c(1:4)), function(x, y) {
#  sample(which(y$Model == x), min(table(data.analysis.comp3$Model)))},
#  y = data.analysis.comp3))
# data.analysis.comp3 <- data.analysis.comp3[sub, ]
fun <- function(x, y, per = .33) {sample(which(y$Model == x), round(table(y$Model)[1]*per))}

sub.test <- unlist(lapply(as.list(paste0(0, c(1:4))), fun,
                          y = data.analysis.comp3))
test2 <- data.analysis.comp3[sub.test, 2:ncol(data.analysis.comp3)]
test1 <- data.analysis.comp3[sub.test, 1]
train <- data.analysis.comp3[-sub.test, ]

for(i in 1:100){
(fit <- randomForest(Model ~ ., data=train, xtest = test2, ytest = test1, 
                    importance=TRUE, ntree=1000, keep.forest = FALSE, replace=FALSE))

predictions <- predict(fit, 
                       a,
                       type="prob")
predictions

save(fit, file=paste0("~/Box Sync/colliding ranges/Simulations_humans/Results/RF_daily_output/RF_daily_output_", format(Sys.time(), format="%d_%b_%Y"), "_",i, "_NoREPLACEMENT_.Rdata"))
}
```

```{r}


save(fit, file=paste0("~/Box Sync/colliding ranges/Simulations_humans/Results/RF_daily_output/RF_daily_output_", format(Sys.time(), format="%d_%b_%Y"),".Rdata"))
```




```{r}

plot(fit, ylim=c(0,1))

```




```{r}
labs <- c("Basic", "+Diffusion", "+Takeover", "+Diffusion +Takeover")

```


```{r}
# bar plot
png("Prob_aus.png", width = 25, height = 25, res = 300, units = "in")
par(mar = c(8, 8, 1, 1))
pred <- setNames(as.numeric(predictions), labs)
cols <- rev(c("darkgreen", "red", "blue", "darkorange1"))
barplot(pred, col = cols, ylab = "Proability", cex.lab = 3, cex.names = 2)
dev.off()
```

![](Prob_aus.png)


```{r}
# Plot confusion matrix
png("Conffusion_matrix_all.png", width = 25, height = 25, res=300, units="in")
par(mar = c(10, 11, 1, 1))
colors1 <- colorRampPalette(colors = c("#f0f0f0", "#bdbdbd","#636363"))
prop <- apply(fit$confusion[, -5], 2, function(x){x / sum(x)}) * 100

image(prop, col = colors1(20), axes=FALSE)
axis(1, at=c(0, .33, .66, 1), labels=labs, tick = FALSE, line = FALSE, cex.axis = 3.5, pos = -.19)
axis(2, at=c(0, .33, .66, 1), labels=labs, tick = FALSE, line = FALSE, cex.axis = 3.5)
mtext("ACTUAL", side = 1, padj = 3, cex = 4)
mtext("PREDICTED", side = 2, padj = -3, cex = 4)

for(i in 1:4) {
  for(j in 1:4) {
    text(x = c(0, .33, .66, 1)[i], y = c(0, .33, .66, 1)[j], paste0(round(prop[i, j], 2), "%"),
         cex = 5)
  }
}
dev.off()
```


![](Conffusion_matrix_all.png)


```{r}
importance(fit)
```


```{r}
# Variables importance

imp <- importance(fit)
imp <- apply(imp, 2, function(x) (x - min(x))/(max(x) - min(x)))
imp <- imp[sort(imp[, 5], index.return = TRUE, decreasing = TRUE)$ix, ]


names <- rownames(imp)
names[names == "spatial.tests.fora"] <- "Space F"
names[names == "spatial.tests.dom"] <- "Space D"
names[names == "sprate"] <- "Sp(ratio)"
names[names == "transition_from_trait_1_to_2"] <- "TR(1-2)"
names[names == "transition_from_trait_2_to_1"] <- "TR(2-1)"
names[names == "Phylogenetic_signal"] <- "PhySig(D)"
names[names == "Evolutionary_distinctiveness_sum"] <- "EDsum"
names[names == "Pylo_diversity_is_sum_of_BL"] <- "PDsum"
names[names == "transition_rate_ratio_1to2_over_2to1"] <- "TR(ratio)"
names[names == "gamma"] <- "Gamma"
names[names == "mean_Phylogenetic_isolation"] <- "MPI"
names[names == "extrate"] <- "Ext(ratio)"
names[names == "average_phylogenetic_diversity_is_mean_of_BL"] <- "PDmean"
names[names == "extinction_per_speciation"] <- "DR"
names[names == "variance_Phylogenetic_isolation"] <- "VPI"
names[names == "F_quadratic_entropy_is_sum_of_PD"] <- "F"
names[names == "Mean_pairwise_distance"] <- "MPD"
names[names == "variance_Pylo_diversity_is_variance_of_BL"] <- "PDvar"
names[names == "variance_pairwise_distance"] <- "VPD"


png("var_import_all.png", width = 25, height = 25, unit="in", res=300)
par(mar = c(10, 18, 1, 1))
plot(x = rev(imp[, 5]), y = 1:nrow(imp), type = "l", yaxt = "n", 
     ylab = "", xlab = "Variable Importance",
     xlim = c(0, 1), lwd = 2, cex.lab = 4)
for (i in 1:nrow(imp)) {
  abline(h = i, lty = 3, col = "gray80")
}
abline(v = seq(0, 1, 1/19), lty = 3, col = "gray80")

lines(x = rev(imp[, 4]), y = 1:nrow(imp), col = "darkgreen", lwd = 2)
lines(x = rev(imp[, 3]), y = 1:nrow(imp), col = "red", lwd = 2)
lines(x = rev(imp[, 2]), y = 1:nrow(imp), col = "blue", lwd = 2)
lines(x = rev(imp[, 1]), y = 1:nrow(imp), col = "darkorange1", lwd = 2)
lines(x = rev(imp[, 5]), y = 1:nrow(imp), lwd = 3)

points(x = rev(imp[, 4]), y = 1:nrow(imp), col = "darkgreen", cex = 2)
points(x = rev(imp[, 3]), y = 1:nrow(imp), col = "red", cex = 2)
points(x = rev(imp[, 2]), y = 1:nrow(imp), col = "blue", cex = 2)
points(x = rev(imp[, 1]), y = 1:nrow(imp), col = "darkorange1", cex = 2)
points(x = rev(imp[, 5]), y = 1:nrow(imp), pch = 20, cex = 3)


text(y = 1:nrow(imp), x = par("usr")[1] - .17, labels = rev(names),
     srt = 0, pos = 4, xpd = T, cex = 4)
dev.off()
```

![](var_import_all.png)




```{r}
par(mfrow=c(2,3))

# Box plots
boxplot(spatial.tests.fora ~ Model, data = data.analysis.comp3)
abline(h = a$spatial.tests.fora, col = "red", lty = 2)

boxplot(spatial.tests.dom ~ Model, data = data.analysis.comp3)
abline(h = a$spatial.tests.fora, col = "red", lty = 2)

boxplot(log(sprate) ~ Model, data = data.analysis.comp3, ylim = c(-10, 10))
abline(h = log(a$sprate), col = "red", lty = 2)

boxplot(log(extrate) ~ Model, data = data.analysis.comp3, ylim = c(-10, 10))
abline(h = log(a$extrate), col = "red", lty = 2)

boxplot(log(transition_rate_ratio_1to2_over_2to1) ~ Model, data = data.analysis.comp3)
abline(h = log(a$sprate), col = "red", lty = 2)

boxplot(Phylogenetic_signal ~ Model, data = data.analysis.comp3, ylim = c(0, 1))
abline(h = a$Phylogenetic_signal, col = "red", lty = 2)


```



```{r}
#build a data tracking table to track parameter changes through time

str(fit)

y

```








































